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Review of CSEDM Data and Introduction of Two Public CS1 Keystroke Datasets

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Title: Review of CSEDM Data and Introduction of Two Public CS1 Keystroke Datasets
Language: English
Authors: Edwards, John, Hart, Kaden, Shrestha, Raj
Source: Journal of Educational Data Mining. 2023 15(1):1-31.
Availability: International Educational Data Mining. e-mail: jedm.editor@gmail.com; Web site: https://jedm.educationaldatamining.org/index.php/JEDM
Peer Reviewed: Y
Page Count: 31
Publication Date: 2023
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
High Schools
Secondary Education
Descriptors: Data Analysis, Computer Science Education, Learning Analytics, Research Methodology, Keyboarding (Data Entry), Barriers, Data Collection, Metadata, Computer Software, Grades (Scholastic), Programming, Undergraduate Students, Majors (Students), College Entrance Examinations, High School Students, Teaching Methods, Student Behavior, Outcomes of Education, Intelligent Tutoring Systems, Physical Characteristics, Computer Security, Plagiarism, Identification, Assignments, State Universities
Geographic Terms: Utah
Assessment and Survey Identifiers: ACT Assessment
ISSN: 2157-2100
Abstract: Analysis of programming process data has become popular in computing education research and educational data mining in the last decade. This type of data is quantitative, often of high temporal resolution, and it can be collected non-intrusively while the student is in a natural setting. Many levels of granularity can be obtained, such as submission, compilation, edit, and keystroke events, with keystroke-level logs being the most fine-grained of commonly used dataset types. However, the lack of open datasets, especially at the keystroke level, is notable. There are several reasons for this failing, with the most prominent being the challenges of deidentification that are peculiar to keystroke log data. In this paper, we present the public release of two fully deidentified keystroke datasets that are the first of their kind in terms of both event and metadata richness. We describe our collection technique and properties of the data along with deidentification techniques that, while not fully relieving researchers of significant effort, at least reduce and streamline manual work in hopes that researchers will release similar datasets in the future.
Abstractor: As Provided
Entry Date: 2023
Accession Number: EJ1383373
Database: ERIC
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  Data: Analysis of programming process data has become popular in computing education research and educational data mining in the last decade. This type of data is quantitative, often of high temporal resolution, and it can be collected non-intrusively while the student is in a natural setting. Many levels of granularity can be obtained, such as submission, compilation, edit, and keystroke events, with keystroke-level logs being the most fine-grained of commonly used dataset types. However, the lack of open datasets, especially at the keystroke level, is notable. There are several reasons for this failing, with the most prominent being the challenges of deidentification that are peculiar to keystroke log data. In this paper, we present the public release of two fully deidentified keystroke datasets that are the first of their kind in terms of both event and metadata richness. We describe our collection technique and properties of the data along with deidentification techniques that, while not fully relieving researchers of significant effort, at least reduce and streamline manual work in hopes that researchers will release similar datasets in the future.
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      – Text: English
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        PageCount: 31
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        Type: general
      – SubjectFull: Computer Science Education
        Type: general
      – SubjectFull: Learning Analytics
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      – SubjectFull: Research Methodology
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      – SubjectFull: Keyboarding (Data Entry)
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      – SubjectFull: Barriers
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      – TitleFull: Review of CSEDM Data and Introduction of Two Public CS1 Keystroke Datasets
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